R Is Still Hot – and Getting Hotter

When I wrote a white paper titled “R Is Hot” about four years ago, my goal was to introduce the R programming language to a larger audience of statistical analysts and data scientists. As it turned out, the timing couldn’t have been bet...

Revolution R Plus

Revolution R Plus is the enhanced and supported distribution of the world's most widely used statistical data analysis software, open source R. A complete platform for data science and building data driven applications, Revolution R Plu...

Free Course: Introduction to Revolution R...

Revolution R Enterprise allows R users to process, visualize, and model terabyte- class data sets at a fraction of the time of legacy products without requiring expensive or specialized hardware. This is an introductory course for accom...

Revolution R Enterprise: Faster Than SAS

In analytics, speed matters. How much? We asked the director of analytics from a leading U.S. marketing services provider, a Revolution Analytics customer. Her team supports more than 1,000 predictive models currently in production; her...

The Revolution Analytics perspective on Big Data

When it comes to Big Data, it’s “one thing to be able to query it, but it’s another thing to be able to actually ask that data meaningful questions,” according to Revolution Analytics head of marketing and community David Smith. The exe...

The Rise of Data Science in the Age of Big Data Analytics: Why data distillation and machine learning aren't enough

Presenters:

The reason why Big Data is important is because we want to use it to make sense of our world. It's tempting to think there's some "magic bullet" for analyzing big data, but simple "data distillation" often isn't enough, and unsupervised machine-learning systems can be dangerous. (Like, bringing-down-the-entire-financial-system dangerous.) Data Science is the key to unlocking insight from Big Data: by combining computer science skills with statistical analysis and a deep understanding of the data and problem we can not only make better predictions, but also fill in gaps in our knowledge, and even find answers to questions we hadn't even thought of yet.

In this talk, David will

Introduce the concept of Data Science, and give examples of where Data Science succeeds with Big Data ... and where automated systems have failed.

Share some thoughts about the future of Big Data Analytics, and the diverging use cases for computing grids, data appliances, and Hadoop clusters

Discuss the skills needed to succeed

Talk about the technology stack that a data scientist needs to be effective with Big Data, and describe emerging trends in the use of various data platforms for analytics: specifically, Hadoop for data storage and data "refinement"; data appliances for performance and production, and computing grids for data exploration and model development.